Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach
In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/7/1752 |
| _version_ | 1797608075314593792 |
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| author | Qingmiao Zhang Hanzhi Dong Junhui Zhao |
| author_facet | Qingmiao Zhang Hanzhi Dong Junhui Zhao |
| author_sort | Qingmiao Zhang |
| collection | DOAJ |
| description | In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences. |
| first_indexed | 2024-03-11T05:38:34Z |
| format | Article |
| id | doaj.art-296274d2ebf64d7b98b8672e61dfa681 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T05:38:34Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-296274d2ebf64d7b98b8672e61dfa6812023-11-17T16:35:07ZengMDPI AGElectronics2079-92922023-04-01127175210.3390/electronics12071752Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network ApproachQingmiao Zhang0Hanzhi Dong1Junhui Zhao2School of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Information Engineering, East China Jiaotong University, Nanchang 330013, ChinaIn high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.https://www.mdpi.com/2079-9292/12/7/1752channel estimationmassive MIMOhigh-speed railwaynoise2noiseconditional generative adversarial networks |
| spellingShingle | Qingmiao Zhang Hanzhi Dong Junhui Zhao Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach Electronics channel estimation massive MIMO high-speed railway noise2noise conditional generative adversarial networks |
| title | Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach |
| title_full | Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach |
| title_fullStr | Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach |
| title_full_unstemmed | Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach |
| title_short | Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach |
| title_sort | channel estimation for high speed railway wireless communications a generative adversarial network approach |
| topic | channel estimation massive MIMO high-speed railway noise2noise conditional generative adversarial networks |
| url | https://www.mdpi.com/2079-9292/12/7/1752 |
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